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SEPTEMBER 4, 2023

In a review published in Engineering, scientists explore the burgeoning field of machine learning (ML) and its applications in chemistry.

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FEBRUARY 3, 2025

The 2025 Machine Learning Toolbox: Top Libraries and Tools for Practitioners Image by Author | Ideogram 2024 was the year machine learning (ML) and

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AWS Machine Learning Blog

APRIL 8, 2025

With the support of AWS, iFood has developed a robust machine learning (ML) inference infrastructure, using services such as Amazon SageMaker to efficiently create and deploy ML models. In this post, we show how iFood uses SageMaker to revolutionize its ML operations.

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JUNE 14, 2023

Learn about some of the best Python libraries for programming Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). A lot of …

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DECEMBER 1, 2024

Artificial Intelligence (AI) and Machine Learning (ML) might feel like buzzwords that are everywhere these days, but there’s a reason for …

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DECEMBER 30, 2024

One of the most talked-about niches in tech is machine learning (ML), as developments in this area are expected to have a significant impact on IT as well as other industries. The field has grown at an extraordinary pace, revolutionizing several industries along the way.

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How to Learn Machine Learning

APRIL 8, 2025

TLDR: In this article we will explore machine learning definitions from leading experts and books, so sit back, relax, and enjoy seeing how the field’s brightest minds explain this revolutionary technology! Yet it captures the essence of what makes machine learning revolutionary: computers figuring things out on their own.

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DECEMBER 5, 2024

Let’s take a look at a concise roadmap to building a lasting and effective machine learning career. The rate at which machine learning (ML) is …

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SEPTEMBER 12, 2023

Graphic processor maker Nvidia joined the rarefied short list of companies valued at $1 trillion-plus earlier this year thanks in no small part to the explosion of interest in, and development of, generative AI and machine learning (ML) applications in Silicon Valley. After all, Nvidia’s GPUs are …

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DECEMBER 2, 2024

To learn more about the ModelBuilder class, refer to Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 1: PySDK Improvements. He works with Amazon.com to design, build, and deploy technology solutions on AWS, and has a particular interest in AI and machine learning.

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SEPTEMBER 16, 2023

Machine Learning (ML), a subfield of Artificial Intelligence (AI), … These third-party libraries are an essential part of any AI developer’s toolkit.

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MARCH 31, 2025

Observability and monitoring is the most cited challenge when moving ML models into production. The Institute for Ethical AI & Machine Learning

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MAY 27, 2025

In 2025, integrating artificial intelligence (AI) and machine learning (ML) into cybersecurity is no longer a futuristic ideal but a functional reality. As cyberattacks grow more complex and targeted, AI technologies have become central to modern defense strategies. These systems detect and

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SEPTEMBER 4, 2023

Home Table of Contents Getting Started with Docker for Machine Learning Overview: Why the Need? How Do Containers Differ from Virtual Machines? Finally, we will top it off by installing Docker on our local machine with simple and easy-to-follow steps. How Do Containers Differ from Virtual Machines?

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AWS Machine Learning Blog

APRIL 10, 2025

Amazon SageMaker HyperPod resilient training infrastructure SageMaker HyperPod is a compute environment optimized for large-scale frontier model training. Frontier model builders can further enhance model performance using built-in ML tools within SageMaker HyperPod. Get started with Amazon SageMaker HyperPod.

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NOVEMBER 26, 2023

Amazon Redshift ML empowers data analysts and database developers to integrate the capabilities of machine learning and artificial intelligence into …

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MARCH 15, 2025

Addressing this challenge, our study introduces a machine learning (ML)-powered search engine specifically tailored for analyzing tera-scale high-resolution mass spectrometry (HRMS) data. Mass spectrometry generates vast amounts of data in chemistry labs.

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JULY 14, 2023

The past few years have seen a surge of interest in using machine learning (ML) methods for studying the behavior of nonhuman animals (hereafter …

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DECEMBER 2, 2024

This long-awaited capability is a game changer for our customers using the power of AI and machine learning (ML) inference in the cloud. The scale down to zero feature presents new opportunities for how businesses can approach their cloud-based ML operations.

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MAY 15, 2025

Qualtrics harnesses the power of generative AI, cutting-edge machine learning (ML), and the latest in natural language processing (NLP) to provide new purpose-built capabilities that are precision-engineered for experience management (XM). Qualtrics refers to it internally as the Socrates platform.

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JULY 5, 2023

Today, we are delighted to introduce the latest version of the AWS Well-Architected Machine Learning (ML) Lens whitepaper. The AWS Well-Architected Framework provides architectural best practices for designing and operating ML workloads on AWS.

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OCTOBER 2, 2023

The more physicists use artificial intelligence and machine learning, the more important it becomes for them to understand why the technology works …

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AWS Machine Learning Blog

SEPTEMBER 18, 2023

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model.

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OCTOBER 9, 2023

This lesson is the 2nd of a 3-part series on Docker for Machine Learning : Getting Started with Docker for Machine Learning Getting Used to Docker for Machine Learning (this tutorial) Lesson 3 To learn how to create a Docker Container for Machine Learning, just keep reading.

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MARCH 6, 2023

With artificial intelligence and machine learning (AI/ML) processors and coprocessors roaring across the embedded edge product landscape, the quest continues for high-performance technology that can run a wide range of AI/ML models with very low power consumption. Brainchip has been marketing a …

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AWS Machine Learning Blog

SEPTEMBER 8, 2023

This post is co-written with Travis Bronson, and Brian L Wilkerson from Duke Energy Machine learning (ML) is transforming every industry, process, and business, but the path to success is not always straightforward. Finally, there is no labeled data available for training a supervised machine learning model.

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JANUARY 24, 2025

Overview of vector search and the OpenSearch Vector Engine Vector search is a technique that improves search quality by enabling similarity matching on content that has been encoded by machine learning (ML) models into vectors (numerical encodings). These benchmarks arent designed for evaluating ML models.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

Data preparation is a crucial step in any machine learning (ML) workflow, yet it often involves tedious and time-consuming tasks. You’ll also see faster performance for transforms and analyses, and a natural language interface to explore and transform data for ML.

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FEBRUARY 13, 2025

AI/ML engineers would prefer to focus on model training and data engineering, but the reality is that we also need to understand the infrastructure and mechanics […]

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APRIL 24, 2025

About the authors Renuka Kumar is a Senior Engineering Technical Lead at Cisco, where she has architected and led the development of Ciscos Cloud Security BUs AI/ML capabilities in the last 2 years, including launching first-to-market innovations in this space. She holds an undergraduate degree in Computer Science & Engineering.

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AWS Machine Learning Blog

FEBRUARY 11, 2025

Get started with SageMaker JumpStart SageMaker JumpStart is a machine learning (ML) hub that can help accelerate your ML journey. He holds a Bachelors degree in Computer Science and Bioinformatics. Marc Karp is an ML Architect with the Amazon SageMaker Service team.

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AWS Machine Learning Blog

MARCH 8, 2023

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. Create a custom container image for ML model training and push it to Amazon ECR.

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AWS Machine Learning Blog

OCTOBER 29, 2024

This engine uses artificial intelligence (AI) and machine learning (ML) services and generative AI on AWS to extract transcripts, produce a summary, and provide a sentiment for the call. This post provides guidance on how you can create a video insights and summarization engine using AWS AI/ML services.

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AWS Machine Learning Blog

MAY 15, 2025

By automating repetitive tasks, SuperAcc enhances both operational efficiency and accuracy, using Apoideas self-trained machine learning (ML) models to deliver consistent, high-accuracy results in live production environments. Creating robust ML models is challenging due to the scarcity of clean training data.

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AWS Machine Learning Blog

FEBRUARY 11, 2025

GraphStorm is a low-code enterprise graph machine learning (ML) framework that provides ML practitioners a simple way of building, training, and deploying graph ML solutions on industry-scale graph data. We encourage ML practitioners working with large graph data to try GraphStorm.

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APRIL 28, 2023

After the release of ChatGPT, artificial intelligence (AI), machine learning (ML) and large language models (LLMs) have become the number one topic of discussion for cybersecurity practitioners, vendors and investors alike. This is no surprise; as Marc Andreessen noted a decade ago, software is …

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NOVEMBER 30, 2023

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and effortlessly build, train, and deploy machine learning (ML) models at any scale. Deploy traditional models to SageMaker endpoints In the following examples, we showcase how to use ModelBuilder to deploy traditional ML models.

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